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Secondary crashes or crashes that occur in the wake of a preceding or primary crash are among the most critical incidents occurring on highways, due to the exceptional danger they present to the first responders and victims of the primary crash. In this work, we developed a self-exciting temporal point process to analyze crash events data and classify it into primary and secondary crashes. Our model uses a self-exciting function to describe secondary crashes while primary crashes are modeled using a background rate function. We fit the model to crash incidents data from the Florida Department of Transportation, on Interstate-4 (I-4) highway for the years 2015-2017, to determine the model parameters. These are used to estimate the probability that a given crash is secondary crash and to find queue times. To represent the periodically varying traffic levels and crash incidents, we model the background rate, as a stationary function, a sinusoidal non-stationary function, and a piecewise non-stationary function. We show that the sinusoidal non-stationary background rate fits the traffic data better and replicates the daily and weekly peaks in crash events due to traffic rush hours. Secondary crashes are found to account for up to 15.09% of traffic incidents, depending on the city on the I-4 Highway., Competing Interests: There are no competing interests for this study., (Copyright: © 2023 Motagi et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Competing Interests: Disclosures of conflicts of interest: J.C. No relevant relationships. S.P. National Science Foundation Graduate Research Fellowship, paid for through Brown University as part of the computer science department, additional stipend from Brown itself; consultant for Spellcraft AI (minor fees [<1000 dollars] for relatively unrelated consulting for a startup, the topic was similar: foundation models, although not in a medical context).

Background: Hereditary transthyretin amyloidosis (ATTRv) is an ultra-rare, life-shortening disease with a high unmet need. This study examined ATTRv caregiver health-related quality of life (HRQoL) and productivity., Methods: A cross-sectional online survey, including EQ-5D-3L, Hospital anxiety and depression scale (HADS), and caregiver and patient characteristics questions, was developed to assess ATTRv caregiver burden. A companion general population survey collected EQ-5D-3L, HADS and chronic health conditions data. Caregiver-control group differences in HRQoL were assessed using t-tests and chi-square tests. Ordinary Least Squares regression was used to estimate the disutility of being a caregiver compared to controls stratified by patient ambulatory status., Results: Thirty-six caregivers and matched controls completed the survey (n = 72). The disease severity of patients they cared for was varied: 33% required no assistance walking, 58% required assistance with walking and 9% required a wheelchair/were bedridden. On average, caregivers spent 6 h daily on practical care and 4 h daily on emotional support. Fifty-six percent indicated that they had changed their employment due to providing ATTRv care. Caregivers reported lower HRQoL, as indicated by lower EQ-5D 3L utility scores (M = 0.772, SD = 0.178 vs. M = 0.849, SD = 0.218) and higher HADS anxiety (9.3 vs. 6.1, p < 0.01) and depression (7.6 vs. 4.4, p < 0.01) scores, compared with matched controls. Caregivers were also more likely to report sleep problems (33% vs. 8%, p < 0.01) and stress (42% vs. 0%, p < 0.001) as chronic conditions than controls., Conclusions: The study results indicate that caring for a person with ATTRv can have a considerable negative impact on caregivers' HRQoL and productivity. The study findings provide important information for economic evaluations of ATTRv treatments., (© 2023. The Author(s).)

This article presents a framework to analyse the gendered impact of COVID-19 on workers in global value chains (GVCs) in the business process outsourcing, garment and electronics industries. Distinguishing between the health and lockdown effects of the pandemic, and between its supply- and demand-related impacts, the authors' gendered analysis focuses on multidimensional aspects of well-being, understands the economy as encompassing production and social reproduction spheres, and examines the social norms and structures of power that produce gender inequalities. Their findings suggest that the pandemic exposes and amplifies the existing vulnerabilities of women workers in GVCs., (© The authors 2021 Journal compilation © International Labour Organization 2021.)